We form a more realistic dataset for disentanglement, which we name MNIST/STL-10. It is similar to Colorful-Moving-MNIST [1]}, but designed for disentanglement. MNIST [2]} contains handwritten digits, and STL-10 is an image classification dataset [3]}.
We create MNIST/STL-10 instances by overlaying a MNIST digit on a STL-10 image. We can vary the following factors: digit class (DC), digit location (DL), or background class (BC). A view pair holds two of three factors constant while the third is varied uniformly at randomExamples can be found in the Appendix with additional dataset details. The dataset has been made publicly available at https://github.com/aburns4/UnsupDisent.
